We are seeking a highly skilled Applied AI Engineer with hands-on expertise in Generative AI, LLMs, AI agents, and modern AI frameworks. The ideal candidate will design, develop, evaluate, and deploy scalable, enterprise-grade AI solutions, with a focus on agentic workflows, LLM evaluation, Spec-Based Development, vector databases, and AWS Bedrock Knowledge Bases.
Key Responsibilities
- Design and deploy AI agents, agentic workflows, and enterprise Generative AI applications.
- Develop, fine-tune, and evaluate LLMs, including Claude, Azure OpenAI, and open-weight models.
- Build RAG solutions using vector databases, graph databases, and enterprise knowledge bases.
- Implement MCP and Agent-to-Agent (A2A) communication.
- Develop AI/ML workflows using AWS SageMaker, Bedrock, MLflow, and Kubeflow on Kubernetes/EKS.
- Build data ingestion, embedding, and multimodal processing pipelines using Python, Spark, and PySpark.
- Integrate AI solutions with enterprise platforms through APIs and standardized architectural patterns.
- Implement LLM evaluation, trace evaluation, safety guardrails, and validation frameworks such as DeepEval.
- Apply CI/CD, Git, Spec-Based Development, and cloud-native engineering best practices.
Required Qualifications
- Bachelor's degree in Computer Science, Data Science, AI/ML, or a related field; Master's preferred.
- Strong proficiency in Python, Pandas, NumPy, and machine learning frameworks such as Transformers and LangChain.
- Hands-on experience with Generative AI, LLMs, prompt engineering, fine-tuning, and AI agent development.
- Experience with AWS, Azure OpenAI, AWS Bedrock, and SageMaker.
- Expertise in RAG architectures, vector databases, knowledge bases, and multimodal AI.
- Experience with LLM evaluation frameworks such as DeepEval and AI safety/guardrails.
- Knowledge of Git, CI/CD, Spec-Based Development, and cloud-native architectures.
- Familiarity with big data technologies such as Spark and Hadoop.
- Strong collaboration skills and experience working in Agile environments.
- GitHub code repository link required for all candidates. Thorough technical vetting is mandatory.
Preferred Qualifications
- Experience in financial services, regulatory technology, or enterprise automation.
- Familiarity with MLOps tools such as MLflow, Kubeflow, and Weights & Biases.
- Experience with Docker, Kubernetes, and Apache Spark.
- Contributions to open-source AI projects or research publications.
- Experience with graph databases and production-grade AI deployments.
Work Conditions
- On-site position in a collaborative, cross-functional Agile environment.
